For some years now, ‘factor investing and smart beta’ has been the hot topic in the financial media. While saturation coverage of almost every sub-theme related to this subject has ranged from analytics by academics of factor performance to longer term implications for the asset management industry, the bulk of the focus has been on the multitude of strategies proposed by each provider.

The biggest plus point from this intense coverage is the growth in investor acceptance. Even ‘traditional’ managers now regularly refer to factor investing or comment on it. It has also led to some standardisation of the associated vocabulary: the names of the main factors (value, momentum, low volatility and quality) are now quite common within financial parlance.

However, as cognitive sciences have taught us, there are drawbacks to a high level of exposure in the media. For instance, the fact that everyone is using the same names for factors creates the impression that we are necessarily speaking of the same thing. This in turn implicitly suggests that factor investing is an asset class, of which single factors are sub-asset classes, just as European equity is a subset of the equity asset class.

This is not the case. The details of specific methodologies can dramatically change the risks and results of factor-based strategies, and should be examined closely to avoid the pitfalls of unwanted risks.

Single factors: neutralisation is key

The naïve approach to single factor investing is to simply select the stocks that have the desired characteristic, and to find a weighting scheme that doesn’t create too many problems in terms of liquidity and diversification, i.e. usually linked, to some extent, to the original market capitalisation. This leads to smart beta strategies that do have some of the desired factor exposure, but that also involve unwanted risk biases related to the nature of the factor or the measure chosen for the factor.

For instance, naïve price momentum will naturally have a highly variable beta: when markets rise, banks outperform, not specifically because they have an intrinsically strong momentum, but because banking is a high beta sector.

On the other hand, defensive sectors outperform in bearish markets, leading to a global beta variability of naïve momentum. Using alpha momentum rather than price momentum would be better.

A naïve low-volatility factor will also have large sector deviations which translate into interest-rate sensitivity, since the least volatile stocks are the most ‘bond-like’.

A naïve value factor, which fundamental indexing would create, can be highly carbon-intensive, because buying cheaper also implies buying ‘real’ economy assets that pollute more. A consumer goods manufacturer is usually more value in style than, for instance, a Fintech and anyone can see it is probably the source of more carbon emissions too.

Much of the discussion about factor timing is related to this conundrum: does factor timing mean the timing of any residual unwanted risks of naïve factor strategies (beta, sector or country deviations) or the timing of the actual true alpha from the factor? We do not need factor strategies to take active positions on these other subjects.

In our view, factor strategies are the most effective when they use a breadth of diversifying indicators, are managed targeting constant risk over time, are beta neutral, macro-sector neutral, region neutral and even unbiased on the basis of market capitalisation exposures, i.e. size neutral. We believe that size, a factor with a strong exposure to the liquidity premium, is better handled separately on account of capacity constraints. All these criteria can improve and stabilise the alpha from factors, although unfortunately, they do not directly yield either an investable strategy or a common vocabulary…

Exhibit 1: Purified alpha compared to raw alpha

Source: THEAM, Dec 2016, World universe, USD monthly simulations

To turn these pure factors into actual investable products, we have chosen to build ‘high octane’, single-factor regional strategies, by maximising the exposure to the factor for a given level of alpha volatility. It might be further from the benchmark than a naïve approach, but it is more in line with what factor investing is really about: choosing the right risks.

Multi-factor: constraints are key

Multi-factor strategies are even more prone to the ‘asset class’ confusion, since they are usually benchmarked on traditional equity asset classes, and therefore look comparable. However, the aim of the strategy and the constraints imposed on the portfolio are the main drivers of the end efficacy of a factor-based strategy.

For instance, if improving the Sharpe ratio is the priority of a multi-factor strategy, reducing the beta is a legitimate bias, which can be attained either by overweighting the low-volatility factor relative to value, momentum and quality, or simply by shorting futures at the level of portfolio construction.

On the other hand, if the information ratio is the priority, focusing the relative risk budget on a balanced allocation to complementary factors purified so that their beta is set to 1 is then more efficient rather than having beta biases generating tracking error.

In long-only benchmarked portfolios, the attainable information ratio for a multi-factor strategy largely depends on the level of tracking error. Factor investing requires creating active positions in the long-only benchmarked portfolio, overweighting some stocks and underweighting other stocks. But increasing tracking error will eventually push the size of those active weights to the limit of what is feasible while remaining long-only. So there is a limit to how much tracking error can be efficiently attained.

On the other hand, at low levels of tracking error, the active weights are small and thus easy to implement. But who cares about a higher information ratio for say 10 basis points of tracking error?

Splitting the universe is another costly constraint which highlights the ‘non-asset class’ behaviour of factor strategies. Market cap benchmarks can add up, but factor investing is more efficient when the opportunity set is larger and more subject to the Law of Large Numbers.

Below we look at our our Diversified Equity Factor Investing (DEFI) strategy as an example. DEFI is an equity multi-factor strategy that builds portfolios invested in value, quality, momentum and low vol stocks. We show the resulting information ratio from applying the exact same strategy to different stock universes.

Exhibit 2: Reduction of Information Ratio when splitting the universe

Source: THEAM, Dec 2016, local currency for regional portfolios and USD for the World portfolio.

Transaction costs, including brokerage, market impact and taxation can also have a significant impact on the efficiency of a factor-based strategy. If environmental, social responsibility and governance (ESG) criteria, or even just carbon emissions, are additional constraints then the question may arise as to whether, at the end of the day, there will be any factor exposure left.

Our research, published in 2014 in the Journal of Asset Management (see “An integrated risk-budgeting approach for multi-strategy equity portfolios”) shows one efficient way out of this conundrum of constraints. In this paper we demonstrate that factor premiums can be still captured efficiently with a constrained multi-factor portfolio as long as the constraints are reasonable and the tracking error remains below sensible levels. The framework presented in the paper relies on separating the search for factor premiums from the question of how to efficiently handle the portfolio constraints facing the practionner. Such a separation is a great tool to help investors navigate between their numerous and often contradictory aims.

As a whole, factor investing is NOT an asset class, neither in single nor multi-factor approaches; rather, it is a tool for discussion, which allows investors to turn academic findings into genuine investment solutions. Such solutions are always a form of compromise, and the whole point is to understand which choices have been made in efficiently reaching a given solution.